ML Platform Engineer
Tasks
- Architect deployment monitoring
- Build scalable ML infrastructure
- Collaborate with product engineering teams
- Collaborate with research teams
- Create ML system abstractions
- Create agent oriented systems
- Debug distributed workloads
- Deploy machine learning models
- Design ML training systems
- Develop internal developer tooling
- Drive platform architecture decisions
- Implement observability for distributed systems
- Implement workflow automation
- Improve workload scheduling
- Manage GPU resources
- Operate production model serving
- Optimize performance and reliability
Perks/Benefits
Skills/Tech-stack
Cloud infrastructure | Datadog | Debugging | Distributed Systems | GPU Computing | GPU scheduling | GitHub Actions | Infrastructure automation | Kubernetes | Linux | Machine Learning | Machine Learning Infrastructure | Model Serving | Monitoring | Observability | Python | Temporal | Terraform | Workflow Orchestration
Education
N/A
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